Method and system for monitoring a condition of cerebral aneurysms

ABSTRACT

Methods and systems for controlling aneurysm initiation or formation in an individual are presented; the technique comprises receiving morphological data of an artery being indicative of at least first and second geometrical parameters of the artery along its trajectory; analyzing the data to identify at least one flow-diverting location along the artery satisfying first and second predetermined conditions of the geometrical parameters; classifying the individual as having or not having disposition for future formation of an aneurysm, depending respectively on whether the at least one flow-diverting location is identified or not and generating classification data; and generating prediction data for the individual with regard to future aneurysm formation.

TECHNOLOGICAL FIELD AND BACKGROUND

This invention is in the field of medical devices and relates to amethod and system for monitoring a condition of aneurysms inindividuals.

An aneurysm is a localized, blood-filled balloon-like bulge in the wallof a blood vessel. Aneurysms in the arterial tree of the brain, calledcerebral aneurysms, or Berry aneurysms typically cause no symptoms whenthey form. A brain aneurysm may rupture and cause an intracranialhemorrhage (called subarachnoid hemorrhage). Once ruptured, aneurysmscause high rates of morbidity and mortality. Depending on the severityof the hemorrhage, brain damage or death may result. Almost halfafflicted individuals die in one month, while third of survivors, havemoderate to severe disability. Cerebral aneurysms are not congenital,but rather develop during life. Besides age, known risk factors, foraneurysms development include female gender, familial preponderance,polycystic kidney disease, the presence of atherosclerotic disease,smoking, and hypertension.

WO2014077360 discloses a system including a memory, an aneurysmidentification device, a distortion-degree evaluation device, and arupture risk derivation device. The memory stores medical image data.The aneurysm identification device identifies an aneurysm in the medicalimage data. The distortion-degree evaluation device quantitativelyevaluates a distortion degree of the aneurysm. The rupture riskderivation device derives a rupture risk of the aneurysm from a resultof the evaluation.

WO2011008906 discloses a computer-aided system which identifies aneurysmsuspects in 3D image datasets. The system takes the raw image dataset asinput and assigns one or more points of interest (POIs) in the imagedata. The system determines one or more features for each POI andidentifies one or more aneurysm suspects from among the assigned POIsbased on the determined features.

WO2010121146 discloses an approach to automatically detecting,classifying and/or highlighting abnormal structures such as brainaneurysms is based on three-dimensional studies of the brain vessels.The approach is applicable to effectively all currently availablemodalities of acquisition of the cerebral vessels, including magneticresonance angiography (MRA), computed tomography angiography (CTA), andconventional catheter-based three-dimensional rotational angiography(3DRA).

-   Lauric A, et al., (J Biomech 22, 3018-3027; 2014) described the    curvature effect on hemodynamic conditions at the inner bend of the    carotid siphon and its relation to aneurysm formation.-   Tutino V M, et al., (Journal of cerebral bloodflow and metabolism    34, 415-424; 2014) showed that increased blood vessel tortuosity    preceded aneurysm formation following bilateral carotid ligation in    rabbit models.-   Piccinelli M, et al., (Neurosurgery 68, 1270-1285; 2011) pointed to    a correlation of the presence of sharp bends in feeding arteries    with aneurysm rupture.-   Kayembe K N, et al., (Stroke 15, 846-850; 1984) previously described    a correlation between variations in the Circle of Willis (a ring at    the base of the brain where basilar artery and the internal carotid    arteries “communicate” with each other) and an increased risk for    developing cerebral aneurysms.

GENERAL DESCRIPTION

There is a need in the art for a novel technique enabling effectiveidentification of individuals with a high risk for aneurysm formation.

The present invention provides a novel and powerful technique foridentifying individuals with a predisposition for developing aneurysmsbefore, even years ahead of aneurysm's development. The identificationof the possibility to developing future aneurysms gives opportunity toprevent the development of aneurysms and saves the population from itsdevastating consequences.

It is noted that the invention focuses on cerebral aneurysms andSaccular aneurysms in particular. Therefore, wherever in thisspecification, the following are interchangeably and equivalently used:“aneurysms”, “cerebral aneurysms”, “intracranial aneurysms”, “Saccularaneurysms” and “Berry aneurysms”.

According to the invention, a noticeable difference in the morphology ofthe arterial network between healthy individuals and aneurysm-developingindividuals is present. Herein below, some non-limiting quantitativemethods/parameters that help in discriminating between the healthy andunhealthy individuals are described. However, the invention is notlimited to one method or another. Any method that identifies thenoticeable differences, e.g. automatic morphological comparison of thearterial tree of an individual, to that of another individual, can beused.

The invention can be utilized to map or screen large populations,without any need for background aneurysm-related information, in orderto identify people with risk for developing cerebral aneurysms, andconsequently save lives and lower drastically the so high economic andsocial burdens associated with the rupture of aneurysms.

The technique of the invention provides for simple, direct measurementof predetermined geometrical parameters of the artery in a healthyindividual (i.e. not aneurysm patient) and provides meaningful resultsas to whether to classify the individual as having disposition forfuture aneurysm formation or not. On the other hand, the invention canalso be used on individual that were/are diagnosed with aneurysms.

According to the novel technique of the invention, the screening of thelarge populations may be done for one time only, providing meaningfulresults for classifying the individuals as lacking or having risk fordeveloping aneurysms in the close or far future. The classification ofan individual as lacking or having disposition for future aneurysmdevelopment is done, according to the invention, with very highprediction certainty. Therefore those individuals who are classified asaneurysm-risk free can continue with their lives normally not worryingabout this issue again.

On the other side, individuals classified as having the very high,almost certain, risk for aneurysm development can then be monitoredperiodically to detect any initiation of the life-threateninganeurysm(s) as early as possible and be treated suitably. Treatingindividuals in order to prevent development of aneurysms is far moresafe and effective than treating people who have already developedaneurysms. Generally, aggressive blood pressure and heart rate control,in people who have the risk for developing aneurysms, may delay or evenprevent aneurysm development. In contrast, people who already havedeveloped aneurysms are nowadays treated, as long as the aneurysms havenot ruptured, with invasive techniques (with all the risk thisintervention carries), such as inserting metal coils into the aneurysmdome. These coils cause thrombus formation inside the aneurysm, therebyminimizing rupture risk.

The inventor has found that there is a correlation between arterialthree-dimensional (3D) geometry and the presence of aneurysms in anindividual. Further, the 3D geometry of the arterial tree is a crucialrisk factor for the formation of aneurysms. Individuals who aresusceptible for developing aneurysms have arterial tree morphology whichis markedly different than that of normal individuals. It is noted thatthe formation of aneurysms is not distributed randomly but ratherfollows certain rules with defined parameters. Aneurysms develop inareas with complex configurations, for example at bifurcationsfulfilling some conditions as will be detailed herein below.

According to the invention, several factors relating to thecharacteristics of the artery are crucial for aneurysm development.These factors include, inter alia, presence of flow-divertingregions/locations, such as sharp bends, along the artery; size(diameter) of the artery at the flow-diverting location; the artery wallcondition at the flow-diverting location and location of the artery inthe body.

As noted, the location of the artery is a first parameter influencingthe formation of aneurysms. The vast majority of aneurysms arise in theintra-dural compartment (the compartment just below the brain and abovethe skull base). Cerebral arteries have very tortuous path beforereaching the lower surface of the brain (while inside the bony skull,and the cavernous sinus). Although cerebral aneurysms may arise in thistortuous path (outside the dura), they are quite rare, and in mostinstances do not rupture. While the invention is not limited to theintra-dural region or even the brain, and it may be practiced withsuitable customization to other arteries in another organs of the body,the invention focuses on the cerebral arteries in the intra-duralcompartment.

The size of the artery, which is generally expressed by the arterydiameter or radius (of the transverse cross-section of the artery), is asecond parameter. The numbers which are given herein below refer to theinternal diameter/radius, as this what the imaging modalities used bythe inventor give. However, it is noted that this should not limit theinvention and the numbers can be adapted for the externaldiameter/radius as the case may be. The inventor has found that Saccularaneurysms arise from medium-sized arteries. Smaller sized arteries donot develop Saccular aneurysms. In particular, cerebral arteries with adiameter less than 1 mm (so-called “hypoplastic” or underdeveloped), areconsidered as small arteries which do not develop aneurysms. Consideringthe intra-dural region of the brain, arterial diameter of arteries uponjoining the lower surface of the brain is about 5˜6 mm, and thisdiameter goes down along the artery. Consequently, the main range inwhich aneurysms develop in the subarachnoid space is from about 1.3 mmto about 6 mm for the internal diameter.

Another important parameter that dictates the development of aneurysmsis the 3D morphological curvature of the artery, such that both thecurvature value and the curve direction, i.e. the direction into whichthe flow is changing its direction, have been found to play significantroles. The inventor has found that cerebral aneurysms develop in aflow-diverting region along the arteries, with high curvature peaksresembling sharp bends in the itinerary of the artery.

According to the novel technique of the invention, the arterycross-sectional size (its diameter/radius) and the curvature value, atthe flow-diverting location, that indicate whether the individual has adisposition for future aneurysm initiation and formation, areco-related. The upper bound/limit of the curvature value, which above itan individual develops aneurysm(s), is dependent on the artery size(diameter/radius), and/or vice versa. In one specific scenario, therelation between the artery size and the curvature upper limit value isa one-dimensional relation, more specifically a direct, linear,relation. The artery size dictates the upper limit value of thecurvature, such that for different artery sizes, above the predeterminedminimal value, corresponding different upper limits for the curvaturevalue exist. In other words, a specific upper limit of the curvaturevalue can be indicative of aneurysm formation for a specific firstartery size, while it will not be indicative of aneurysm formation foranother specific artery size, typically larger than the first arterysize.

It is noted that, the aneurysms develop mainly in an aneurysm directionopposite to the curve direction. The curve direction may be defined asthe direction of a vector pointing in a normal direction to the tangentvector pointing in the direction of the flow inside the artery, at eachspecific point in the curve. The normal vector points towards thedirection along which the tangent vector changes its direction. Inanother words, the normal vector is defined as the derivative of thetangent vector with respect to the arclength parameter of the curve. TheFrenet-Serret frame system could be used with the invention with thesame nomenclature to facilitate understanding, i.e. the tangent vectorand the normal vector of the Frenet-Serret frame are the same as thetangent and normal vectors described herein above. The calculation ofthe curvature of the artery may be done by calculating the curvature ofa centerline of the artery. The centerline is defined as the linecomposed of the centers of spheres inscribed in the artery at each pointalong its axis, sequenced by flow direction.

The aneurysm develops only if an artery wall exists in the opposite tothe normal vector direction as described above. Moreover, it has beenfound that aneurysms develop only in the maximal centrifugal forcesregion, on the convex, not concave, side of the curve.

Individuals with the above-mentioned factors develop aneurysms, in thehigh curvature region, while those who do not meet these criteria donot. In other words, as described above, there is a threshold of anupper bound/limit for the artery curvature, being dependent on thearterial size (diameter/radius), which is breeched byaneurysm-developing individuals but not breeched by healthy individualswho do not develop aneurysms. In normal, healthy, individuals thearteries turn in a smooth way keeping the curvature below a threshold ofan upper limit. The diagnosis of aneurysm developing and non-developingindividuals is unchanged when comparing individuals with larger headcircumferences to those with smaller ones. It is similar in both sexes,and does not change with age.

In the invention, the definition of an artery includes the “parent”artery and dominant branches that bifurcate from it, at each bifurcationin the arterial tree. When an artery bifurcates to two or more branches,one of these “daughter” arteries is dominant, being the branch whichdiverts the flow of the parent artery. If there is no bifurcation, ananeurysm develops in the flow-diverting location along the artery. Theparent artery's flow does not affect other branches which are consideredas new parent arteries starting at the current bifurcation, the otherbranches are subject to similar rules as the parent artery from whichthey bifurcated. According to the invention, these rules apply to alldaughter branches in every bifurcation.

As has been said, it is possible to test each individual, and to screenlarge populations, to find whether an individual has these arterialaberrations and may be susceptible (with very highpossibility/disposition) to aneurysm formation.

Thus, according to one broad aspect of the invention, there is provideda method for controlling aneurysm initiation or formation in anindividual, the method comprising:

receiving input data comprising morphological data of an arterycomprising data indicative of at least first and second geometricalparameters of the artery along its trajectory;

analyzing the input data to identify at least one flow-divertinglocation along the artery satisfying first and second predeterminedconditions of the at least first and second geometrical parameters; and

classifying the individual as having or not having disposition forfuture formation of an aneurysm in a point on a wall of the artery,depending respectively on whether the at least one flow-divertinglocation is identified or not, and generating classification data; and

generating, based on the classification data, prediction data for theindividual with regard to future aneurysm formation.

It should be understood that morphological data includes data aboutgeometry (in a 3D space) of an artery within a brain portion (e.g. atleast an intra-dural brain portion). Such data about the artery isindicative of the geometry (size and shape) of the artery along itstrajectory. The prediction for formation of an aneurysm is based onidentifying one or more predetermined locations (having certainpredefined properties) in the artery and analyzing the one or morelocations to determine whether the geometrical data at the locationsatisfies a condition for aneurysm formation.

In some embodiments, receiving of the input data comprising receivingthe first and second geometrical parameters comprising artery size andartery curvature along the artery's trajectory.

In some embodiments, the first and second predetermined conditionscomprise respective first and second predetermined threshold values ofthe first and second geometrical parameters.

In some embodiments, analyzing of the input data comprisesidentification of the first predetermined condition of the firstgeometrical parameter as being a precondition for identification of thesecond predetermined condition of the second geometrical parameter.

In some embodiments, analyzing of the input data comprisesidentification of the first predetermined condition being apredetermined minimal value of the first geometrical parameter andidentification of the second predetermined condition being apredetermined maximal value which below an aneurysm will not form andwhich above an aneurysm will form.

In some embodiments, the first and second predetermined conditions ofthe at least first and second geometrical parameters have apredetermined relation there between.

In some embodiments, the predetermined relation is a linear relationbetween the first and second geometrical parameters.

In some embodiments, the artery comprises each dominant artery branchbifurcating at each bifurcation site along the artery, the dominantartery branch being a branch which diverts the flow of the artery.

In some embodiments, the wall of the artery is at a convex side of theartery.

In some embodiments, the artery is a cerebral artery and the aneurysm isa cerebral aneurysm.

In some embodiments, the cerebral artery is located in an intra-duralbrain region.

In some embodiments, the artery size has a diameter value of 1.3 mm orgreater.

In some embodiments, analyzing of the input data comprises applying aFrenet-Serret frame analysis along a centerline of the artery, tothereby identify the at least one flow-diverting location along theartery. The centerline may comprise point centers of spheres inscribedin the artery. The centerline may be defined as a line with a directiondefined by flow direction.

In some embodiments, the morphological data is obtained from image dataindicative of a cerebral arterial tree. The image data may be indicativeof a three-dimensional image of the cerebral arterial tree.

In some embodiments, the cerebral arterial tree comprises a plurality ofcerebral arteries, each of the plurality of cerebral arteries starts atan entrance into intra-dural brain region or at a bifurcation site inthe cerebral arterial tree.

According to another broad aspect of the present invention, there isprovided a system for controlling aneurysm initiation or formation in anartery, the system comprises:

a data input utility configured and operable for receiving image data ofthe artery;

a data processing utility configured and operable for:

-   -   analyzing the image data and generating morphological data        comprising at least first and second geometrical parameters of        the artery at each point along its trajectory,    -   identifying in the morphological data a flow-diverting location        along the artery, the flow-diverting location along the artery        having predetermined first and second conditions of the first        and second geometrical parameters, and    -   classifying the artery as having or not having disposition for        future formation of an aneurysm in a point on a wall of the        artery; and

an output utility configured and operable to generate output dataindicative of the classification.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to better understand the subject matter that is disclosedherein and to exemplify how it may be carried out in practice,embodiments will now be described, by way of non-limiting example only,with reference to the accompanying drawings, in which:

FIG. 1A is a diagram flow illustrating one method in accordance with theinvention;

FIG. 1B illustrates classification of individuals based on geometricalparameters used in the method of the invention;

FIG. 2 schematically illustrates the method of centerlines calculatedfor an arterial network;

FIG. 3 schematically illustrates analysis of an arterial networkincluding parent arteries, dominant arteries and other arteries;

FIG. 4 schematically illustrates application of the Frenet-Serret frameto an arterial network; and

FIG. 5 schematically illustrates a system in accordance with theinvention.

DETAILED DESCRIPTION OF EMBODIMENTS

Reference is made to FIG. 1A illustrating a method 10, according to theinvention, for use in detection of disposition for aneurysm initiationor formation in an individual and classifying the individual as havingor lacking disposition for aneurysm future formation. The method 10includes, inter alia, examining an artery to identify a condition forpredicted formation of an aneurysm in the artery. The method 10exemplifies the invention being applied to the cerebral arteries. Itshould be noted, however, that the method could be applied to anotherartery or artery network/tree in the body with appropriate adjustment ofthe values of parameters which will be described herein after. Themethod includes, inter alia, the steps of obtaining input includingmorphological data of the artery (step 12); analyzing the input data toidentify at least one flow-diverting location along the artery (step 14)being stress-concentration region(s); classifying the individual ashaving or not having disposition for future formation of an aneurysm ina point on a wall of the artery (step 16); and generating outputprediction data about the individual with respect to disposition foraneurysm future formation (step 18).

In the figure, optional and/or non-limiting exemplary steps and/or stepsthat can be performed with various modalities not necessarily describedherein and which do not form part of the invention, are highlighted withdashed boxes.

It should be understood that, the input data including the morphologicaldata belongs usually to an individual with no known aneurysm-relatedpast, i.e. the individual is any person in the population. The inventionis directed at revealing those who have a disposition for aneurysmformation. However, the invention can be equally practiced onindividuals with aneurysm-related past, such as individuals who had orhave aneurysms.

The acquisition of the input data of the individual, including themorphological data (step 12) can be done by utilizing image data of thebrain (step 12A) which can be obtained by known methods in the art,including but not limited to computed tomography (CT), magneticresonance imaging (MRI), both of which can be used with a contrastspecified agent(s), and cerebral angiography. It should be noted thatthe acquisition of the image data is not part of the invention, and theinvention is not limited to the above mentioned modalities and it can beused with any modality, known or will be developed in the future, forobtaining morphological data of the concerned artery tree, as long asthe modality gives a three-dimensional (3D) image data or morphologicaldata.

As said, while this does not the limit the invention, the focus in thedescribed method is made on the brain artery tree and more specificallythe artery network of the intra-dural compartment of the brain, as thisis the compartment in which the majority of aneurysms develop.Generally, the image data (step 12A) includes data of differentanatomical parts of the brain and not only the arteries, such as theveins and the brain tissue. In this case, a segmentation procedure (step12B) is carried out in order to isolate the data relating to thearterial network from the data relating to all other anatomical figuresin the brain which are irrelevant for the aneurysm analysis.

As will be described further below, the morphological data (step 12C) ofinterest for the subsequent analysis step (step 14), includes suchgeometrical parameters as the artery(ies) size(s) along the arterytrajectory and the artery(ies) curvature value(s) along the arterytrajectory. The artery size parameter can be represented by its diameteror radius along its trajectory.

After acquisition of the 3D morphological data of the artery/arterynetwork in the intra-dural compartment, the diameter(s) of the arteriesin the artery network is determined and those arteries with a diameterbeing above a predetermined value are selected (step 14A) for furtheranalysis, whereas the arteries with a diameter less than thepredetermined value are dropped and excluded from further analysis. Itis to be noted that, while this step of deselecting arteries (step 14A)is shown in the figure as belonging to the data analysis step (step 14),this step can be performed previously and already directly included inthe input data provided (step 12). The inventor, as well as otherresearchers in the field, have found that arteries with a diameter of 1mm or less do not develop aneurysms. Since the arteries entering theintra-dural compartment have a diameter of about 5˜6 mm at the entrypoint, for the purposes of identifying formation of cerebral aneurysms,the lower and upper threshold values for artery selection is chosen tobe in the range of about 1.3-6 mm. It should be noted, that foraneurysms in arteries in other in parts other than the intra-duralcompartment the range changes accordingly. While, the inventors have notchecked with other regions, the invention is not limited to theintra-dural compartment and to the above-mentioned range for arteryselection.

After focusing on the arteries in the intra-dural compartment of thebrain which have a diameter between 1.3-6 mm, the next importantgeometrical parameter for identifying the disposition of an individualto develop aneurysm(s) in the wall of an artery is the artery'scurvature. The three-dimensional curvature is a well-establishedgeometrical parameter and can be measured by different known techniques.Further below, additional details about the specific technique which wasused by the inventor can be found. One non-limiting example of a way tocalculate the curvature of the arteries is by optionally computing anddrawing their centerlines (step 14B) as will be further described below.

As described above, at each location along the artery where the diameterfulfills a predetermined artery threshold value, e.g. being at leastabout 1.3 mm, the artery curvature at that specific location ismeasured/calculated to look for a curvature threshold value (step 14C).If the measured curvature value is equal to or exceeds the predeterminedcurvature threshold value, the individual is classified as havingdisposition for aneurysm future formation in the artery wall at thespecific location along the artery where the two threshold values aremet (step 16). And, if the measured curvature value is less than thepredetermined curvature threshold value, the individual is classified asnot having disposition for aneurysm future formation in the artery wallat the specific location along the artery (step 16).

Reference is made to FIG. 2 illustrating an example of an artery network40 having bifurcations, e.g. 42, with the centerlines 44 calculated andshown passing at the middle (center) along the longitudinal axis of eachartery. The calculation of arterial centerlines 42, in this non-limitingexample, is based on the “Voronoi diagram”, which simulates inflating“balloons” inside the arteries with the balloons' centers being thecenterlines of the arteries. Centerlines are powerful descriptors of theshape of vessels and are determined as weighted shortest paths tracedbetween two extremal points. In order to ensure that the final lines arein fact central, the paths cannot lie anywhere in space, but are boundto run on the Voronoi diagram of the vessel model, considered as theplace where the centers of maximal inscribed spheres are defined.Centerlines are determined as the paths defined on Voronoi diagramsheets that minimize the integral of the radius of maximal inscribedspheres along the path, which is equivalent to finding the shortestpaths in the radius metric.

A typical artery tree would contain bifurcations at which a specificartery splits into several other branches stemming out of thebifurcation. Referring to FIG. 3 , there is shown an example of anartery network 60, containing several bifurcations 68. According to theinvention, during the calculation of the centerlines, the arteries ateach bifurcation are analyzed and divided to three artery categories. Ateach bifurcation, the entering “parent” artery 62 splits into two kindsof “daughter” arteries, a “dominant” artery 64 and “secondary” (or“other”) arteries 66. The “dominant” artery 64 is the main branchbifurcating from the “parent” artery 62, into which the main blood flowdiverts. During the calculation of the centerlines in the arterynetwork, the centerline of the parent artery 62 continues into thedominant artery 64. Therefore, the dominant artery 64 is considered as acontinuing part of the parent artery 62 which entered into thebifurcation 68. The secondary artery 66 (one or more) is considered as anew independent artery starting at the bifurcation and analysis iscarried on it as a new parent artery. Therefore, a new centerline startsfor the secondary artery at the bifurcation and at each new bifurcationthis secondary parent artery splits again into a dominant artery beingits continuation and at least one new secondary artery.

Turning back to FIG. 1A, one non-limiting example formeasuring/calculating the curvature by utilizing centerlines isdescribed. In step 14B, the centerlines are processed and the curvatureof the three-dimensional centerlines is calculated to identifyflow-diverting locations, i.e. sharp bends with certain characteristicas will be described below, along the artery network.

The curvature of a regular space curve C in three dimensions (andhigher) is, as in the case of curves in two dimensions, the magnitude ofthe acceleration of a particle moving with unit speed along a curve.Thus, if γ(s) is the arc length parameterization of C then the unittangent vector T(s) is given by: T(s)=γ(s)

The curvature is the magnitude of the acceleration:

The direction of the acceleration is the unit normal vector N(s), whichis defined by:

The plane containing the two vectors T(s) and N(s) is called theosculating plane to the curve at γ(s). The curvature has the followinggeometrical interpretation. There exists a circle in the osculatingplane tangent to γ(s) whose Taylor series to second order at the pointof contact agrees with that of γ(s). This is the osculating circle tothe curve. The radius of the circle R(s) is called the radius ofcurvature, and the curvature is the reciprocal of the radius ofcurvature:

The tangent, curvature, and normal vector together describe thesecond-order behavior of a curve near a point. In three-dimensions, thethird order behavior of a curve is described by a related notion oftorsion, which measures the extent to which a curve tends to move in ahelical path in space. The torsion and curvature are related by theFrenet-Serret formulas (in three dimensions) and their generalization(in higher dimensions). As these centerlines consist of discrete points,and curvature is not defined for discrete points, the centerlinecurvature is defined based on finite difference.

As described above, the inventor has found that the curvature valueshould not exceed a predetermined threshold (upper bound) value innormal people, and wherever the curvature value is equal to or exceedsthe predetermined threshold (upper bound) value, then if one morecondition is met as will be described further below, an aneurysm willdevelop at the sharp bend location.

When both above-described conditions are met, i.e. the diameter of theartery and the curvature peak value (measured for example along thecenterline) are equal to or above the respective threshold values, theFrenet-Serret frame can be applied to that point (at the flow-divertinglocation) in order to obtain information about the curvedirection/location (point) on the artery wall where the aneurysm willdevelop, which is given by the opposite direction to the direction ofthe normal vector of the Frenet-Serret frame. The normal vector is thevector normal to the flow direction. According to some non-limitingembodiments of the invention, an examination is carried out in thedirection opposite the direction of the normal vector to check if anartery wall resides in the direction opposite the direction of thenormal vector. If the check is positive, then the location of the pointon the artery wall at which a vector opposite the normal vectorintersects with the artery wall is marked as a location of futureaneurysm development. The location of the intersection point resemblesthe way the flowing blood would go out of the itinerary because of thesharp curve. It is also clear, that if an aneurysm already exists inthat direction then the invention provides also a way to detect existinganeurysms as well. However, if the vector opposite the normal vectorpoints in a direction where a branch at a bifurcation starts, then noaneurysm will form and the blood will flow into that branch.

Reference is made to FIG. 4 illustrating the application of theFrenet-Serret frame analysis to the centerlines of an exemplified artery70 with a blood flow going upwards and to the right and left side of thefigure. As shown, the centerline 72 passes roughly along the centralaxis of the artery 70 which at a bifurcation 74 bifurcates into twobranches, a dominant branch 70 (to the right side) and another secondarybranch 74 (to the left side) that is regarded as a new artery subject toa new analysis according to the invention, with its own centerline 76.The Frenet-Serret frame is applied along the centerlines 72 and 76. Inthe figure, the Frenet-Serret frame is applied at four exemplary points81, 83, 85 and 87. In practice, the Frenet-Serret frame may be appliedat each point along the centerline. Alternatively, seeking to saveeffort and time, the frame is applied only at points of the centerlinehaving a curvature value peak of at least 0.3 mm⁻¹ (for arteries in theintra-dural compartment).

As illustrated, at each point along the centerline, the Frenet-Serretframe includes 3 vectors, the tangent vector 82 pointing in thedirection of the blood flow, the normal vector 84 pointing in the curvedirection, i.e. the direction of change in the direction of the tangentvector 82 (and being in the same plane with the tangent vector), and abi-normal vector 86 pointing in an orthogonal direction both to thetangent and normal vectors such that it is the cross product of thetangent and normal vectors. According to the invention, a forth oppositevector 88 which points in the opposite direction to the normal vector 84(and being in the same plane with the tangent and normal vectors) pointsin the direction of an already developed aneurysm, or afuture-developing aneurysm if and only if an artery wall is present inthe opposite vector direction. As mentioned earlier, an aneurysm wouldform only if the first condition applies, i.e. if the curvature value isabove the predetermined curvature threshold value.

In the specific Frenet-Serret frame examples shown, the frame atlocation 81 illustrates an already formed aneurysm 90. The aneurysm'saxis 94, along which the aneurysm 90 has developed, points in the samedirection of the opposite normal 88. The frames at locations 83 and 87illustrate two examples of two locations at which an artery wall 92 ispresent in the direction at which the opposite vector points. In boththese locations (83 and 87), an aneurysm will develop, given that thecondition of the curvature value, which depends on the artery size, ismet (e.g., a curvature value of at least 0.3 mm⁻¹ in the intra-duralcompartment). In the frame at location 85 (at the bifurcation 72), theopposite normal vector points in the direction of the secondary artery74, there is no artery wall in the direction pointed to by the oppositevector 88 and therefore no aneurysm would develop at the location 85,even if the curvature value is 0.3 mm⁻¹ or higher.

As mentioned above, one possible way is to check all cerebralvasculature in the predefined arterial size (e.g. diameter) ranges andlocations, and for every point in arterial centerlines to look for highcurvature points, and whenever a high curvature value is detected,checking whether an arterial wall is present in the opposite to thenormal vector direction. Mostly, it is difficult to notice these elbowpoints. The sharp bend is usually less than 2 mm long and most 3Dreconstruction algorithms smooth it away. This fact makes it crucial fora system/tool to detect this sharp bend. Another advantage to thissystem is its ability to diagnose small aneurysms. Small aneurysms aredifficult to diagnose by experienced neuro-radiologists because they areonly few voxels in diameter. In fact, the sensitivity for detectinganeurysms smaller than 5 mm, using an MRI, is less than 50%. The presentinvention solves this problem by looking for sharp bends, not aneurysms,and hence, it is able to detect these small abnormalities. Another wayto appreciate these sharp bends, in most individuals, is by registering(aligning) one arterial 3D reconstruction to another. Registration ofthese two datasets makes the difference between a sharp bend and a lowcurvature in normal individuals visible to the naked eye.

The inventor of the present invention has found that there is a relationbetween the artery size value and the artery curvature threshold value.According to this relation, a given artery size value determines thethreshold value of the artery curvature, above which an aneurysm is mostlikely to develop, and under which no aneurysm will develop. Therefore,the threshold value of the curvature is dependent on the specific arterysize (as long the latter is above the predetermined threshold valuewhich is about 1.5 mm for the artery diameter). FIG. 1B illustrates therelation between the artery size and curvature parameters that was foundby the inventor based on the method of the invention The graph 20illustrates an artery size-curvature function 28 that describes therelation between the two geometrical parameters. In the figure, thegraph 20 illustrates the arterial peak curvature (y-axis) plottedagainst the arterial radius (x-axis) in the same point/location alongthe artery. As shown, the threshold value for the arterial radius isabout 0.65 mm, as shown by the illustrative line 24, above which ananeurysm may form and below which no aneurysm forms. As a result, theexamination of the aneurysm formation is only done for arterial radiusabove about 0.65 mm. The figure illustrates the inventor's finding andconclusion that the relation between the artery size and the thresholdvalue for the curvature is linear. The straight “Berry line” 28 is afunction, which discriminates healthy from abnormal arterialconfiguration. Those above the line 28 are classified as havingdisposition for aneurysm formation while those under the line 28 areclassified as healthy individuals that do not have disposition foraneurysm formation. The parameters of the straight line 28 are:

a=−0.28; b=0.515;

so, the line's function is:(curvature threshold)=−0.28*(artery radius at the same point)+0.515.

According to the invention, the line 28 classifies people. For everyindividual, if the peak curvature point is identified and plottedagainst the radius at this point, then if the point is located above theline 28, the individual will develop an aneurysm, otherwise, theindividual will not.

The line 28 yields:

Positive predictive value (PPV)=0.9432; and

Negative predictive value (NPV)=0.8913.

This means that the line 28 has a very strong classifying indication.

Reference is made to FIG. 5 illustrating one non-limiting example of asystem configured according to the invention. The system 100 can beconfigured with a variety of elements configured and operable to performthe method described above. It should be understood that the belowdescribed example is one among many that can be used according to theinvention. The system 100 is generally a computerized system (softwareor hardware or both) which includes at least the following modules andutilities: an input utility 102 for receiving input image data from asuitable imaging system 200; a processing utility 104 for analyzing theinput image data, by utilizing, inter alia, the method of the invention,as described above; and an output utility 106 for outputting data to theuser. In some embodiments, the imaging system 200 may form an integralpart of the system 100. However, generally, the imaging system is anindependent system providing image data to the system 100 via its inpututility 102. The output utility 106 may include a graphical userinterface/display 140 for presenting the processing utility 104'soutput(s) to the user. In some embodiments, the output utility 106 mayinclude a speaker for delivering suitable sound alerts to the user.

The processing utility 104 includes modules configured to process theinput image data to eventually find locations along the arterial wallsat which a future aneurysm will develop. Possibly, also to findlocations of already developed aneurysms, especially small developedaneurysms which might be difficult to diagnose by the current knownimaging methods and systems. Each of the modules may be softwareconfigured to run on a dedicated hardware. Alternatively, the modulesmay be independent pieces of software configured to run on a regularcomputer.

The processing utility 104 is configured and operable to analyze theimage data to extract the morphological data of interest, including atleast geometrical parameters as the artery's size and curvature at eachpoint along the artery; analyze the geometrical parameters, such that ateach point along the artery, for every artery size above a predeterminedthreshold value (e.g. 1.3 mm), check whether the artery curvature atthat point fulfills the artery size-curvature function, described abovein FIG. 1A; and classify the individual as having or no havingdisposition for future aneurysm development based respectively onwhether the curvature value satisfies the curvature's threshold value,as acquired by the artery size-curvature function, or not. Below,different modules of the processing utility to achieve its purpose aredescribed.

The first module 110 is an “image to morphology data” processing modulewhich receives as an input the image data and outputs as an output themorphological data and possibly three-dimensional reconstructedimage/artery model. The image to morphology data module 110 includesvarious sub-modules such as cropping 112, segmentation 114 andreconstruction 116 sub-modules. The cropping 112 of the image data isperformed as needed, keeping only the relevant image data of thespecific region of interest in the body, e.g. the circle of Willis andcalculating the diameter of arteries to keep arteries larger than 1.3 mmin diameter. The image to morphology data module 110 then performssegmentation 114 of the cropped data, if needed, i.e. to remove allnon-artery tissue. Then, the image to morphology data module 110performs three-dimensional reconstruction 116 of the segmented data suchthat a three-dimensional model of the artery network could be presentedvia the output utility, together with aneurysm data as will be furtherdetailed below.

The second module 120, the aneurysm predictor/detector, can include suchsub-modules as bifurcation identifier 122, centerline calculator 124,curvature value calculator 126, and Frenet-Serret calculator 128. Thebifurcation identifier 122 receives the morphological data andidentifies bifurcations in the artery network. The centerline calculator124 then computes the centerlines of the arteries in the artery networkby identifying, at each bifurcation, the parent artery (the one enteringthe bifurcation according to the blood flow direction), the dominantbranch forming a continuation for the parent artery and each secondarybranch stemming out of the bifurcation thus forming a new parent arteryto which the analysis is carried out independently. The curvature valuecalculator 126 then calculates values of the centerline's curvature toenable identifying flow-diverting locations having curvature valuesfalling within the required range. This is done in as short as possiblestep distances along the centerlines. The curvature value calculator isconfigured to utilize the arterial size-curvature function, as describedin FIG. 1A, to thereby determine the threshold curvature value for eachartery size along the artery's trajectory. The Frenet-Serret calculator128 applies the Frenet-Serret frame to the points identified withcurvature values higher than the threshold value and computes theopposite normal vector to thereby identify the presence of an arterywall in the path given by the opposite normal vector's direction. If awall exists, then aneurysm(s) will form and the opposite.

Eventually, the processing utility 104 marks the locations of futureaneurysms along the artery walls, and the output utility 106 outputs thethree-dimensional constructed artery network, the centerlines, theFrenet-Serret frame and the locations on the artery walls susceptiblefor aneurysm formation to the user.

The classifier 130 is configured and operable to receive the analysisdata as processed by the module 120 and generate classification dataabout the individual that include the individual's disposition fordeveloping aneurysm(s) at each potential location in his brain arterialnetwork. If more than one potential location is identified, theclassifier 130 is configure to rank the plurality of potential locationbased on the severity and/or time-expectancy for developing aneurysm ateach of the potential locations. Consequently, the classifier 130 can beconfigured to generate recommendation data about the recommendedfrequency of monitoring the individual having the disposition foraneurysm formation, based on the number and/or severity of theindividual's diagnosis.

The inventors have performed experiments to validate the technique ofthe invention. Model validation and statistical analysis were performedusing online available datasets of normal individuals, compared againstthose of patients harboring cerebral aneurysms. Morphological data wasobtained from brain images (step 12A in method 10). Normal datasets werecomposed of MRI brain images of healthy volunteers. These were comprisedof images of the brain of subjects in which 20 patients were scanned perdecade (18-29, 30-39, 40-49, 50-59, and 60+), with each group equallydivided by sex. The datasets used, were of brain Magnetic Resonanceangiography (MRA) acquired at 0.5×0.5×0.8 mm3. MRI images were firstcropped, using a software tool, keeping the circle of Willis andarteries larger than 1.3 mm in diameter. The second step wassegmentation (step 12B, removing all non-artery tissue). MRA wassegmented based on voxel intensity, using single threshold. This stepwas implemented using the same tool (a software), used for cropping.Following segmentation, a 3D reconstruction of the circle of Willis, wasperformed using the Vascular Modeling Toolkit (VMTK). These 3Dreconstructions were compared against 102 datasets provided by theAneurisk project. Ten datasets, in which the aneurysm involved more thanhalf the parent artery circumference, were excluded. Mean age for theaneurysm group was 53.9; there were 62 females and 37 males. 55 of thecases were of ruptured cerebral aneurysms, while 43 were unruptured.Individual ages were 26-85. Most aneurysms (93%) were located in theanterior circulation, while 7% were in the posterior circulation (Thebasilar artery or the posterior cerebral arteries). This corresponds toaneurysms distribution by location, in the general population, with morethan 90% of aneurysms located in the anterior circulation. The tool usedto analyze arterial centerlines (step 14B), is the VMTK, which is acollection of libraries or image-based modeling of blood vessels. TheVMTK is an open source tool, and was used in numerous studies in thepast. The VMTK takes as an input the MRA images, or 3D reconstruction ofcerebral arteries from any other modality and calculates arterialcenterlines. Since sharp bends are very short (Mean: 0.779 mm, STD:0.461), the centerlines were resampled to a resolution of 200 samplesper mm. The inventors found this number to give the best results for thecurvature estimation. Overall, more than 30 million point curvatureswere calculated for both groups. Since 1D representation, of arterialcurvature, using arterial centerline, simplifies calculations, theinventors performed all calculation on arterial centerlines.

Arterial 3D curvature (kappa or k) is a basic term in differentialgeometry, and it correlated with cerebral aneurysms initiation. One ofthe issues with calculating arterial curvature peaks, lays in the factthat arterial curvature is derived from the centerline's first andsecond derivatives, and these are very sensitive to noise. This fact isamplified by the fact that, MRA data is composed of discrete points (agrid of 3D points or voxels) and 3D arterial curvature is a continuousmeasure. Various low-pass filters were applied to vessel centerlines tomitigate digitization errors, these are ultimately arbitrary and affectthe value of the curvature obtained; the more severe the filtering, thesmoother the centerline and the lower the measured curvature. Using lessaggressive filter falsely identifies noise as sharp bend in arterialgeometry and does not allow true arterial curvature peaks to beisolated. In addition, the inventors used two different imagingmodalities for curvature estimation, while keeping the processingalgorithm the same. This step was taken in order to preserve thecurvature difference between the two groups. For this reason, the samesmoothing parameters were used for both groups. The inventor calculatedthe centerlines on aneurysms datasets, with the aneurysm in place. TheVMTK calculates the centerlines based on the Voronoi diagram and fits amaximum inscribed sphere in the artery, whose center is the centerline'spoints. As long as there were, at least half the Original artery left,the VMTK is not significantly affected by aneurysms presence and thefiltering process smooths the noise introduced away. Since arterialcurvature and torsion determine its path, these sharp bends cause markeddeviation of a cerebral artery, in the arterial segment, past thecurvature peak (unless there is a second peak, which compensates, foundin about 3% of cases). This is visible to the naked eye, by registeringan aneurysm sequence to a normal one. This fact was used to validate thetechnique of the invention. Whenever there arises a doubt regarding thesmoothing or reconstruction algorithm, a registration of the currentdataset to another dataset, makes the difference visible.

Curvature peaks in arterial regions, where there is no vessel wall to beinfluenced by flow diversion, were excluded. The tool described,calculates the normal vector in each arterial curvature peak region, andchecks to see if arterial wall exists in the opposite normal vectordirection.

Calculated maximal 3D curvatures (mm⁻¹) were recorded for all datasets,as well as curvature peak length (mm) for curvature peaks larger than0.3 mm⁻¹. Mean curvature peaks for all normal datasets was comparedagainst the mean curvature peak, for all included aneurysms datasets.Student T test was used to compare the two peak curvature means.

Thus, the invention provides novel system and method for predictingfuture aneurysm formation. According to the invention, when severalconditions are met an aneurysm will form. The conditions include valueof size of the artery (typically, about 1.3 mm or more); curvature valueof the centerline/wall of the artery is high (typically, 0.3 mm⁻¹ forcerebral arteries in the intra-dural compartment); and presence of anartery wall in the path the blood would follow, by deviating from theartery itinerary, because of the high curvature present at that point.The path may be found by calculating the three-dimensional direction ofthe vector opposite the normal vector, to the flow direction, calculatedin accordance with the Frenet-Serret frame principles.

The invention claimed is:
 1. A method for detecting aneurysms in acerebral artery network of an individual, the method comprising:identifying, in one or more cerebral arteries of the cerebral arterynetwork, one or more flow diverting-regions susceptible for aneurysmdevelopment, by: receiving input data comprising morphological data ofthe cerebral artery network, the morphological data comprising dataindicative of at least first and second geometrical parameterscomprising artery cross-sectional diameter and artery curvature of theone or more cerebral arteries; and analyzing said input data, andidentifying the one or more flow-diverting regions satisfying at leasttwo conditions comprising a first predetermined value of said arterycross-sectional diameter and a second predetermined value of said arterycurvature; and analyzing each identified flow-diverting region andidentifying an aneurysm development direction in the flow-divertingregion, and upon detecting a localized increase in the arterycross-sectional diameter in the aneurysm development direction,determining that an aneurysm exists at said flow-diverting region. 2.The method according to claim 1, wherein said aneurysm developmentdirection is identified as being a direction of a vector opposite to anormal vector to blood flow direction, in the flow-diverting region. 3.The method according to claim 1, wherein said detected localizedincrease in the artery cross-sectional diameter in the aneurysmdevelopment direction is indicative of the aneurysm having a dimensionof a few millimeters.
 4. The method according to claim 1, wherein saidanalyzing of said input data comprises identification of said firstpredetermined value of the artery cross-sectional diameter as being aprecondition for identification of said second predetermined value ofthe artery curvature.
 5. The method according to claim 1, wherein saidanalyzing of said input data comprises identification of said firstpredetermined value of the artery cross-sectional diameter being apredetermined minimal cross-sectional diameter value, and identificationof said second predetermined value of the artery curvature being apredetermined maximal curvature value which below an aneurysm will notdevelop and which above an aneurysm will develop.
 6. The methodaccording to claim 1, wherein said first and second predetermined valuesof respectively said artery cross-sectional diameter and arterycurvature have a predetermined relation there between, being a linearfunction.
 7. The method according to claim 1, wherein said cerebralartery comprises each dominant cerebral artery branch bifurcating ateach bifurcation site along the cerebral artery, said dominant cerebralartery branch being a branch which diverts blood flow of the cerebralartery.
 8. The method according to claim 1, wherein said cerebral arteryis located in an intra-dural brain region.
 9. The method according toclaim 1, wherein said analyzing of said input data comprises applying ananalysis along a centerline of said cerebral artery being indicative ofblood flow direction, to thereby identify said one or moreflow-diverting regions along the cerebral artery.
 10. The methodaccording to claim 1, wherein said morphological data is obtained fromimage data indicative of a three-dimensional image of the cerebralartery network.
 11. A computerized system for detecting aneurysms in acerebral artery network of an individual, the computerized systemcomprising: a data input utility configured and operable for receivingimage data of said cerebral artery network; a data processing utilityconfigured and operable for: analyzing said image data and generatingmorphological data comprising artery cross-sectional diameter and arterycurvature along trajectory of one or more cerebral arteries of thecerebral artery network; identifying, in said one or more cerebralarteries, one or more flow-diverting regions susceptible for aneurysmdevelopment and characterized by at least two conditions comprising afirst predetermined value of said artery cross-sectional diameter and asecond predetermined value of said artery curvature; and analyzing eachidentified flow-diverting region and identifying an aneurysm developmentdirection in the flow-diverting region, and determining that an aneurysmexists at said flow-diverting region upon detecting a localized increasein the artery cross-sectional diameter in the aneurysm developmentdirection; and an output utility configured and operable to generateoutput data indicative of existing aneurysms.
 12. The computerizedsystem according to claim 11, wherein said data processing utility isconfigured and operable to identify said aneurysm development directionas being a direction of a vector opposite to a normal vector to bloodflow direction, in the flow-diverting region.
 13. The computerizedsystem according to claim 11, wherein said data processing utility isconfigured and operable to detect said localized increase in the arterycross-sectional diameter, in the aneurysm development direction, being afew millimeters.
 14. The computerized system according to claim 11,wherein said processing utility is configured and operable to detectwhether, at each point along the cerebral artery's trajectory, theartery cross-sectional diameter value and the artery curvature valuesatisfy a predetermined relation there between, being a linear function.15. The computerized system according to claim 11, wherein said cerebralartery comprises each dominant cerebral artery branch bifurcating ateach bifurcation site along the cerebral artery, said dominant cerebralartery branch being a branch which diverts blood flow of the cerebralartery.
 16. The computerized system according to claim 11, wherein saidcerebral artery is located in an intra-dural brain region.
 17. Thecomputerized system according to claim 11, wherein said identifying ofsaid one or more flow-diverting regions comprises applying an analysisalong a centerline of each cerebral artery being indicative of bloodflow direction, to thereby identify said at least one flow-divertinglocation along the cerebral artery.